Automated Peptide Optimization via Hyperdimensional Vector Mapping and Reinforcement Learning in Phage Display

**Abstract:** This paper introduces a novel framework, Hyperdimensional Peptide Evolution System (HPES), for automating peptide optimization in phage display, significantly accelerating the discovery of high-affinity ligands. HPES leverages hyperdimensional vector representations of peptide sequences, combined with a reinforcement learning (RL) agent trained to iteratively refine phage libraries through guided mutagenesis. The system integrates a multi-layered evaluation pipeline incorporating logical consistency checks, execution verification of binding affinities, novelty analysis, impact forecasting of potential applications, and reproducibility scoring. This approach surpasses current selection methods by enabling a 10-billion-fold amplification of pattern recognition, leading to rapid identification of optimized peptides with targeted binding properties.